| Autor: |
Kanawade, B. R., Zaware, S. N., Nandre, Janvi, Mahale, Yashashree, Dhake, Khushi |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
International Journal of Intelligent Systems and Applications in Engineering; Vol. 11 No. 2 (2023); 262-266 |
| ISSN: |
2147-6799 |
| Popis: |
Pneumonia, which is caused by Streptococcus Pneumoniae, can be deadly if undetected or mistreated. The most common approach for detecting Pneumonia is to have a professional radiologist review a chest X-ray picture, which takes longer and is less reliable. Professionals and physicians can employ computer-assisted diagnosis to solve this problem. Computer-assisted diagnosis might improve doctors' ability to make quick and accurate judgments. Convolutional neural networks that are abbreviated as CNNs have become particularly popular in disease classification due to the usefulness of algorithms in deep learning for the analysis of medical images. The performance of some pre-trained CNN models was examined to reach the final result, followed by an ensemble of top-performing models. The study revealed that putting together various pre-trained CNN models can improve detection accuracy, with the best accuracy being 94.39%. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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